Care ally-assisted massage for Veterans with chronic neck pain: TOMCATT results.

PURPOSE: Chronic neck pain (CNP) is prevalent and challenging to treat. Despite evidence of massage's effectiveness for CNP, multiple accessibility barriers exist. The Trial Outcomes for Massage: Care Ally-Assisted vs. Therapist Treated (TOMCATT) study examined a care ally-assisted massage (CA-M) approach compared to a waitlist control prior to a study design modification (WL-C0). METHODS: CA-M consisted of in-person training for veteran/care-ally dyads to learn a standardized 30-minue massage routine, instructional DVD, and printed treatment manual. Participants were to complete three care ally-assisted massage sessions weekly for 12-weeks. Outcomes collected at baseline, 1-, 3-, and 6-months included validated measures of neck pain severity and associated disability. Linear mixed-model approaches were used for analysis with 3-months as the primary outcome timepoint. RESULTS: Participants (N = 203) were 56.7 ± 14 years old, 75% White, 15% female, and 75% married/partnered. Among 102 CA-M participants, 45% did not attend the in-person training and subsequently withdrew from the study and were more likely to be younger (p = .016) and employed (p = .004). Compared to WL-C0, CA-M participants had statistically significant reductions in pain-related disability at 3-months (-3.4, 95%CI = [-5.8, -1.0]; p = .006) and 6-months (-4.6, 95%CI = [-7.0, -2.1]; p < .001) and pain severity at 3-months (-1.3, 95%CI = [-1.9, -0.8]; p < .001) and 6-months (-1.0, 95%CI = [-1.6, -0.4]; p = .007), respectively. CONCLUSION: In this analysis, CA-M led to greater reductions in CNP with disability and pain severity compared to WL-C0, despite treatment engagement and retention challenges. Future work is needed to determine how to better engage Veterans and their care-allies to attend CA-M training.

Effects of covered solid sorbent tube sample holders on organic vapor measurements.

A study was conducted to examine whether there are significant differences between organic vapor concentrations measured using charcoal tubes with three different configurations: uncovered sample holder (open tube), SKC, and Buck brand covered sample holders. A fractional factorial experimental design was used with the following factors and levels: vapor (n-hexane vs. m-xylene), pump type (pulsating vs. continuous), exposure profile (variable vs. constant), flow rate (30 mL/min vs. 200 mL/min), duration (30 min vs. 80 min), and sample placement (mannequin vs. free hanging). Two of each sampler configuration (six total) were placed in an exposure chamber, and a dynamic test-atmosphere generation system was used to prepare atmospheres containing approximately 12-15 ppm n-hexane or m-xylene with exposure profiles and sampling conducted according to a run sheet generated for the experimental design. A total of 24 runs were completed with six samplers per run, yielding 144 samples that were analyzed by gas chromatography/flame ionization detector. Concentration results for each pair of SKC and Buck covered sample holders were averaged and normalized by dividing by the average result for the open tube sampler from the same run to eliminate the effect of daily variation in chamber concentrations. The resulting ratio of covered sample tube holder and open tube concentrations was used as the response variable. Results of analysis of variance using the general linear model (MINITAB 16) identified statistically significant main effects and/or interactions for pump type, exposure profile, flow rate, and sample holder. However, the magnitude of the effects was generally less than 10%, and overall mean concentration ratios were 0.989 and 1.02 for the Buck and SKC sample holders, respectively. These results show good agreement between covered sample holder results and open tube measurements and demonstrate that exposure assessment errors resulting from the use of covered sorbent tube sample holders for organic vapor monitoring are relatively small (<10%) and not likely to be of practical importance.

Dimensional analysis of actigraphic derived sleep data.

Nonlinear dimensional analyses can be a useful tool in understanding the underlying behavior of dynamical systems, including biological systems. Many biological functions can be modeled as chaotic processes, including sleep. Sleep data can be obtained from several methods, such as electroencephalograms, polysomnography, and actigraph. Actigraphy, because of its low level of invasiveness, is an increasingly popular method of obtaining sleep data. This study analyzed actigraphy data with nonlinear dimensional analyses to determine if such analytic methods would be useful in sleep studies. Participants wore actigraphs on their wrists, which recorded movement for several days. Several sleep quality variables, such as movement during sleep and total sleep time, were derived from these sleep data. These variables were used to determine whether the quality of sleep was good or poor. Lagged phase space plots were graphed and nonlinear parameters for the fractal dimension and the correlation dimension were computed for each participant. Descriptive and inferential statistics were performed to determine if the nonlinear parameters showed significant differences with respect to sleep quality.

A statistical test to determine the quality of accelerometer data.

Accelerometer data quality can be inadequate due to data corruption or to non-compliance of the subject with regard to study protocols. We propose a simple statistical test to determine if accelerometer data are of good quality and can be used for analysis or if the data are of poor quality and should be discarded. We tested several data evaluation methods using a group of 105 subjects who wore Motionlogger actigraphs (Ambulatory Monitoring, Inc.) over a 15 day period to assess sleep quality in a study of health outcomes associated with stress among police officers. Using leave-one-out cross-validation and calibration-testing methods of discrimination statistics, error rates for the methods ranged from 0.0167 to 0.4046. We found that the best method was to use the overall average distance between consecutive time points and the overall average mean amplitude of consecutive time points. These values gave us a classification error rate of 0.0167. The average distance between points is a measure of smoothness in the data, and the average mean amplitude between points gave an average reading. Both of these values were then normed to determine a final statistic, K, which was then compared to a cut-off value, K(C), to determine data quality.